Learning PySpark

Tomasz Drabas, Denny Lee

• Verlag: Packt Publishing
• Jahr: 2017
• ISBN:
  9781786463708
• Auflage: 1
• Thema: Ciencia de la computación
• Thema: Minería de datos

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

About This Book

• Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0
• Develop and deploy efficient, scalable real-time Spark solutions
• Take your understanding of using Spark with Python to the next level with this jump start guide

Who This Book Is For

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What You Will Learn

• Learn about Apache Spark and the Spark 2.0 architecture
• Build and interact with Spark DataFrames using Spark SQL
• Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
• Read, transform, and understand data and use it to train machine learning models
• Build machine learning models with MLlib and ML
• Learn how to submit your applications programmatically using spark-submit
• Deploy locally built applications to a cluster

In Detail

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark.

You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command.

By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Style and approach

This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.

Learning PySpark

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Table of Contents

Learning PySpark

Credits

Foreword

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Understanding Spark

What is Apache Spark?

Spark Jobs and APIs

Execution process

Resilient Distributed Dataset

DataFrames

Datasets

Catalyst Optimizer

Project Tungsten

Spark 2.0 architecture

Unifying Datasets and DataFrames

Introducing SparkSession

Tungsten phase 2

Structured Streaming

Continuous applications

Summary

2. Resilient Distributed Datasets

Internal workings of an RDD

Creating RDDs

Schema

Reading from files

Lambda expressions

Global versus local scope

Transformations

The .map(...) transformation

The .filter(...) transformation

The .flatMap(...) transformation

The .distinct(...) transformation

The .sample(...) transformation

The .leftOuterJoin(...) transformation

The .repartition(...) transformation

Actions

The .take(...) method

The .collect(...) method

The .reduce(...) method

The .count(...) method

The .saveAsTextFile(...) method

The .foreach(...) method

Summary

3. DataFrames

Python to RDD communications

Catalyst Optimizer refresh

Speeding up PySpark with DataFrames

Creating DataFrames

Generating our own JSON data

Creating a DataFrame

Creating a temporary table

Simple DataFrame queries

DataFrame API query

SQL query

Interoperating with RDDs

Inferring the schema using reflection

Programmatically specifying the schema

Querying with the DataFrame API

Number of rows

Running filter statements

Querying with SQL

Number of rows

Running filter statements using the where Clauses

DataFrame scenario – on-time flight performance

Preparing the source datasets

Joining flight performance and airports

Visualizing our flight-performance data

Spark Dataset API

Summary

4. Prepare Data for Modeling

Checking for duplicates, missing observations, and outliers

Duplicates

Missing observations

Outliers

Getting familiar with your data

Descriptive statistics

Correlations

Visualization

Histograms

Interactions between features

Summary

5. Introducing MLlib

Overview of the package

Loading and transforming the data

Getting to know your data

Descriptive statistics

Correlations

Statistical testing

Creating the final dataset

Creating an RDD of LabeledPoints

Splitting into training and testing

Predicting infant survival

Logistic regression in MLlib

Selecting only the most predictable features

Random forest in MLlib

Summary

6. Introducing the ML Package

Overview of the package

Transformer

Estimators

Classification

Regression

Clustering

Pipeline

Predicting the chances of infant survival with ML

Loading the data

Creating transformers

Creating an estimator

Creating a pipeline

Fitting the model

Evaluating the performance of the model

Saving the model

Parameter hyper-tuning

Grid search

Train-validation splitting

Other features of PySpark ML in action

Feature extraction

NLP - related feature extractors

Discretizing continuous variables

Standardizing continuous variables

Classification

Clustering

Finding clusters in the births dataset

Topic mining

Regression

Summary

7. GraphFrames

Introducing GraphFrames

Installing GraphFrames

Creating a library

Preparing your flights dataset

Building the graph

Executing simple queries

Determining the number of airports and trips

Determining the longest delay in this dataset

Determining the number of delayed versus on-time/early flights

What flights departing Seattle are most likely to have significant delays?

What states tend to have significant delays departing from Seattle?

Understanding vertex degrees

Determining the top transfer airports

Understanding motifs

Determining airport ranking using PageRank

Determining the most popular non-stop flights

Using Breadth-First Search

Visualizing flights using D3

Summary

8. TensorFrames

What is Deep Learning?

The need for neural networks and Deep Learning

What is feature engineering?

Bridging the data and algorithm

What is TensorFlow?

Installing Pip

Installing TensorFlow

Matrix multiplication using constants

Matrix multiplication using placeholders

Running the model

Running another model

Discussion

Introducing TensorFrames

TensorFrames – quick start

Configuration and setup

Launching a Spark cluster

Creating a TensorFrames library

Installing TensorFlow on your cluster

Using TensorFlow to add a constant to an existing column

Executing the Tensor graph

Blockwise reducing operations example

Building a DataFrame of vectors

Analysing the DataFrame

Computing elementwise sum and min of all vectors

Summary

9. Polyglot Persistence with Blaze

Installing Blaze

Polyglot persistence

Abstracting data

Working with NumPy arrays

Working with pandas' DataFrame

Working with files

Working with databases

Interacting with relational databases

Interacting with the MongoDB database

Data operations

Accessing columns

Symbolic transformations

Operations on colum...